face="微软雅黑">Notes
of Coursera-MachineLearning-Andrew NG

data-wiz-span="data-wiz-span">Week1-2014/03/07-hphp

data-wiz-span="data-wiz-span">

data-wiz-span="data-wiz-span">欢迎赐教、讨论、转载，转载请注明原文地址~

data-wiz-span="data-wiz-span">Machine Learning
Introduction

data-wiz-span="data-wiz-span">Supervise
learning

face="微软雅黑">how
to pick a model ? straight line or polynomial
? 

• >Regression:
  Predict continous valued output


• >Classification
  problem  ,




• >tumor size Vs
  malignant


• >Tumor size ,
  age , Vs malignant or benign ,


• >c color="#009300">ould
  use more features to predict ( or regression ) : uniformity of cell shape ,
  cell size ......


• >Statistically


• >compromized :
  妥协的

data-wiz-span="data-wiz-span">Unsupervised
Learningdata-wiz-span="data-wiz-span">?

>

• >clustering
  problem




• >google news ,
  with one news , several diff urls are laid.

align="" src="https://img.php1.cn/3cd4a/189d8/978/7dbdf0f38ad53545.jpeg"
>

• >astronomical
  data analysis


• >Cocktail party
  problem




• >seperate voice
  source


• align="" src="https://img.php1.cn/3cd4a/189d8/b64/5b34b53b79a39fdd.jpeg"
  >


• use
  octave , could solve the problem quickly and briefly

data-wiz-span="data-wiz-span">Linear Regression with
one variable

• Model
  representation

>

Training
set : m : number of training examples , x :  input , y : output
variable ,

data-wiz-span="data-wiz-span">y = h(x) , h
: hypothesis

data-wiz-span="data-wiz-span">How do we represent
H?

face="微软雅黑"> data-wiz-span="data-wiz-span">htheta(x) = theta0 +
theta1(x)

data-wiz-span="data-wiz-span">univariant -- linear
regression (a fancy name)

• Cost
  Function

data-wiz-span="data-wiz-span">htheta(x) = theta0 +
theta1(x)

data-wiz-span="data-wiz-span">how to choose two
theta s

>

data-wiz-span="data-wiz-span">choose thetas so that
h(x) is close to given
examples.

 
   
   
   
   
   
   
   
   
   
    >m

data-wiz-span="data-wiz-span">minimize   
  =   1/2m Sum ( h(xi) - yi
) 2

data-wiz-span="data-wiz-span">theta0, theta 1
           
  1

>

face="微软雅黑"> data-wiz-span="data-wiz-span">squared error function  -- the most
common coss function in regression
.

• >Cost function
  intuition I - lecture 7

color="#ff6820" face="微软雅黑">get
better intuition what cost function is doing , and why we want to use
it.

data-wiz-span="data-wiz-span">recap : focus , say
briefly

>

data-wiz-span="data-wiz-span">simplified : theta 0
= 0

data-wiz-span="data-wiz-span">h(x) = theta1 *
x

data-wiz-span="data-wiz-span">J(theta1)  =
1/2m * Sum[i:1-m](theta1xi - yi) 2

data-wiz-span="data-wiz-span">when theta1 = 1
, J
( theta1 ) = 0

data-wiz-span="data-wiz-span">theta1 = 0.5 , J (
theta1 ) = 0.5 , J ( 0 ) =
14/6 

border="0" src="https://img.php1.cn/3cd4a/189d8/b64/5b34b53b79a39fdd.jpeg"
>

• Cost
  function intuition II - lecture 8

border="0" src="https://img.php1.cn/3cd4a/1eebe/cd5/ed19db63ee478b98.png"
>

• contour
  plots : outline


• theta0,
  theta1 != (0, x) or (x, 0) , with the cost function act as a 3D bowl ,
  below

border="0" src="https://img.php1.cn/3cd4a/1eebe/cd5/bcafc120671304eb.webp"
>

• using : contour plots (
  or contour figures ) .

border="0" src="https://img.php1.cn/3cd4a/1e618/cd5/af17da15769ccb2e.jpeg"
>

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